By searching the given image in Yandex gives us the similar photos could be found in the "molecularshirts.com"
To find the picture we provided i decided to download all the photos in the site.I automated the process by using this script.
import os
file = open("molecules","r")
for line in file:
uri= "http://www.molecularshirts.com/wp-content/themes/poza-child/images/names/"+line[:-1]+".jpg"
os.system("wget "+uri)
When the script running on another terminal i used this script to compare photos with the provided image.
import cv2
import numpy as np

original = cv2.imread("1.png")
file = open("molecule_names","r")
for line in file:
image_to_compare = cv2.imread(line[:-1]+".jpg")

if original.shape == image_to_compare.shape:
print("The images have same size and channels")
difference = cv2.subtract(original, image_to_compare)
b, g, r = cv2.split(difference)

if cv2.countNonZero(b) == 0 and cv2.countNonZero(g) == 0 and cv2.countNonZero(r) == 0:
print("The images are completely Equal")
print("The images are NOT equal")

sift = cv2.xfeatures2d.SIFT_create()
kp_1, desc_1 = sift.detectAndCompute(original, None)
kp_2, desc_2 = sift.detectAndCompute(image_to_compare, None)

index_params = dict(algorithm=0, trees=5)
search_params = dict()
flann = cv2.FlannBasedMatcher(index_params, search_params)

matches = flann.knnMatch(desc_1, desc_2, k=2)

good_points = []
for m, n in matches:
if m.distance < 0.6*n.distance:

number_keypoints = 0
if len(kp_1) <= len(kp_2):
number_keypoints = len(kp_1)
number_keypoints = len(kp_2)

print("File Name:"+line)
print("GOOD Matches:", len(good_points))
print("How good it's the match: ", len(good_points) / number_keypoints * 100)

result = cv2.drawMatches(original, kp_1, image_to_compare, kp_2, good_points, None)

Then Voila!

Original writeup (https://github.com/berkeakil/CTF-Writeups/tree/master/2019/RedPwn/Forensics/Molecule%20Shirts).