Identifying microsatellite instability (MSI) in whole slide images (WSIs), one of the most widely used diagnostic imaging formats, is of great importance and in demand. In this study we employed color-based texture features to predict MSI on both a tile and sample-based (patient) level. We found that within cancer cohorts of hematoxylin and eosin (H&E) stained WSIs, texture morphology was able to predict MSI on a tile level with an AUC of up to 0.95 and on a sample level with an AUC of up to 0.98. This runs in contrast to other methods for predicting MSI in H&E WSIs which either utilized artificial intelligence-based models or achieved lower accuracy scores. Our results demonstrate that texture morphology is a major factor for identifying MSI in H&E WSIs and should be used when constructing future models for MSI identification in a clinical setting.