We found that angle featurization was crucial to improving performance for predicting the bandgap for both datasets, suggesting that this graph representation is key for both general and polymorphic property prediction. Angle featurization lead to significant improvement in the ability of both OCELOT-and OMDB-trained models to predict the bandgaps of the polymorphs of ROY, going from an MAE of 0.491 to 0.222 eV and 0.345 to 0.109 eV, respectively. This is a decrease of 54.8 % and 68.4 % in MAE, respectively. Angle featurization also lead to significant improvement in the ability of both OCELOT-and OMDB-trained models to predict the bandgap of an augmented version of the PAH101 dataset, going from an MAE of 0.523 to 0.324 eV and 0.655 to 0.426 eV, respectively. This is a decrease of 38.0 % and 35.0 %, respectively. For polymorphs of the PAH101 dataset, the models trained on the OMDB with angle featurization performed best with the lowest MAE of 0.382 eV. Angle featurization resulted in a decrease in MAE by 34.9 % for OCELOT trained models and by 34.0 % for OMDB trained models.