Trying to figure out the best way to approach this problem with Computer Vision
I am skeptical whether computer vision is going to be the most effective approach, but you can certainly do it.  There is lots of work on image similarity metrics using neural networks (Siamese networks, triplet loss, etc.) and you could certainly implement any of those architectures, acquire a few thousand labels, and train on it.  I would suggest pre-training on some standard similarity dataset (e.g., start with LPIPS) and then fine-tune on your data.  But be prepared that this might or might not be effective.
It might be more effective to extract features and then build a ML model that works with those features rather than with the raw images.  For instance, OCR both images and compare them; compute L1 and L2 distance; etc.