Speaker anonymization aims at hiding a speaker's identity while preserving prosodic and linguistic content. In this work, a voice conversion based speaker anonymization is used, where a source speaker's voice is transformed to that of the target speaker. We observe that the choice of a suitable target speaker is crucial for successful anonymization. If the chosen target speaker is already similar to the source speaker, the anonymization fails with no fault of the anonymization process, as favorably the most dissimilar target speaker for a source speaker should be chosen. However, for every new source speaker, the model could fail to find one. Therefore, we examine the prosody-related acoustic features, such as F0, power envelope, etc., instead of only relying on the speaker dissimilarity, known as speaker embeddings, which might not be available during the anonymization process. While speaker embeddings are model dependent, acoustic features are independent of the anonymization model and ergo, aide in finding optimal target speaker.