Radiomics is a promising mathematical tool for characterizing disease and predicting clinical outcomes from radiological images such as CT. Photon-counting-detector (PCD) CT provides improved spatial resolution and dose efficiency relative to conventional energy-integrating-detector CT systems. Since improved spatial resolution enables visualization of smaller structures and more details that are not typically visible at routine resolution, it has a direct impact on textural features in CT images. Therefore, it is of clinical interest to quantify the impact of the improved spatial resolution on calculated radiomic features and, consequently, on sample classification. In this work, organic samples (zucchini, onions, and oranges) were scanned on both clinical PCD-CT and EID-CT systems at two dose levels. High-resolution PCD-CT and routine-resolution EID-CT images were reconstructed using a dedicated sharp kernel and a routine kernel, respectively. The noise in each image was quantified. Fourteen radiomic features of relevance were calculated in each image for each sample and compared between the two scanners. Radiomic features were plotted pairwise to evaluate the resulting cluster separation of the samples by their type between PCD-CT and EID-CT. Thirteen out of 14 studied radiomic features were notably changed by the improved resolution of the PCD-CT system, and the cluster separation was better when assessing features derived from PCD-CT. These results show that features derived from high-resolution PCD-CT, which are subject to higher noise compared to EID-CT, may impact radiomics-based clinical decision making.