Semantic and task-oriented Vehicle-to-Everything (V2X) communications have been recently proposed to address the scalability challenges of future V2X networks by focusing on the relevance of the exchanged information. Relevance captures the impact of the transmitted information meaning on the intended receiver’s task by taking into account the intended receiver’s context, e.g., its position, speed, and planned driving intentions. Due to its context-dependent nature, relevance can greatly vary across different driving scenarios and different intended receivers, as each vehicle experiences unique context conditions. Hence, selecting the content of the transmitted messages to provide all intended receivers with the required relevant information can be a challenging task, especially in the broadcast V2X communication domain where each transmitting vehicle has multiple intended receivers. This work analyses the impact of different content-selection strategies on the performance of semantic and task-oriented V2X communications. The obtained results show that content-selection strategies play a critical role, particularly under constrained communication scenarios, and that their impact can significantly vary under different driving scenarios. Our analysis highlights key tradeoffs in the content-selection task and offers valuable insights for the design of effective content-selection strategies able to maximize the performance of semantic and task-oriented V2X communications.