Here, we examine the influence of incorporating angle featurization into the graph representation of organic molecular crystals on the performance of property prediction of OSCs. We adapt the M3GNet angle featurization and graph convolution update for this purpose. We train our models on both the OMDB and OCELOT databases, with and without angle featurization, as well as fine-tune models trained on the OMDB with the OCELOT database. We examine the performance of these models on several test cases: the highly polymorphic ROY and the PAH101 dataset, which we supplement with several polymorphs of the original entries in the dataset. We find that angle featurization demonstrably improves the performance of the models across all the test cases examined here. , has the highest R 2 value of 0.925 while Figure , the OCELOT database without angle featurization, has the lowest R 2 value of 0.76. On the other hand, the OCELOT database with angle featurization, Figure , has the lowest MAE of 0.121 eV while the OMDB without angle featurization, Figure , has the highest MAE of 0.251 eV. While angle featurization seems important for improving performance for training on both datasets it seems have more impact on the performance on the OCELOT database. This makes intuitive sense as the OCELOT database unlike the OMDB has several hundred sets of polymorphs, implying that incorporating this featurization is crucial to accurately describing polymorphs accurately in GNNs. While both models without angle featurization struggle at the extremes of each dataset, the OCELOT trained models struggle in the lower bandgap region while the OMDB trained models struggle in the higher bandgap region. This could be due to the data distribution, shown in Figure where the OMDB appears to have more structures in the lower bandgap region and the OCELOT database appears to have more structures in the higher bandgap region.